{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using CNN for dogs vs cats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are going to create a model to enter the [Dogs vs Cats](https://www.kaggle.com/c/dogs-vs-cats-redux-kernels-edition) competition at Kaggle."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 25,000 labelled dog and cat photos available for training, and 12,500 in the test set that we have to try to label for this competition. According to the Kaggle web-site, when this competition was launched (end of 2013): *\"**State of the art**: The current literature suggests machine classifiers can score above 80% accuracy on this task\"*. So if you can beat 80%, then you will be at the cutting edge as of 2013!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import torch.nn as nn\n",
    "from torch.autograd import Variable\n",
    "import torchvision\n",
    "from torchvision import models,transforms,datasets\n",
    "import torch\n",
    "import bcolz\n",
    "import time\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import imp\n",
    "import utils; imp.reload(utils)\n",
    "from utils import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.4.1'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check if GPU is available and if so use it by default, otherwise use CPU only."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using gpu: True \n"
     ]
    }
   ],
   "source": [
    "use_gpu = torch.cuda.is_available()\n",
    "print('Using gpu: %s ' % use_gpu)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data processing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can download the full dataset from Kaggle directly.\n",
    "\n",
    "Alternatively, Jeremy Howard provides a direct link to the catvsdogs [dataset](http://files.fast.ai/data/dogscats.zip). He's separated the cats and dogs into separate folders and created a validation folder as well. You'll need this folder structure to run VGG.\n",
    "\n",
    "For test purpose (or if you run on cpu), you should use the (small) sample directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "01_intro_DLDIY.ipynb  01_intro_INRIA.ipynb  \u001b[0m\u001b[01;34m__pycache__\u001b[0m/  utils.py  \u001b[01;35mvgg16.png\u001b[0m\r\n"
     ]
    }
   ],
   "source": [
    "%ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/ubuntu/dataflowr/Notebooks'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#to be done only once!\n",
    "#%cd /home/ubuntu/data/\n",
    "#!wget http://files.fast.ai/data/dogscats.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!unzip dogscats.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%cd dogscats/\n",
    "#%ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/data/dogscats/sample\n",
      "\u001b[0m\u001b[01;34mtrain\u001b[0m/  \u001b[01;34mvalid\u001b[0m/  \u001b[01;34mvgg16\u001b[0m/\r\n"
     ]
    }
   ],
   "source": [
    "%cd /home/ubuntu/data/dogscats/sample/\n",
    "%ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Warning to be modified!\n",
    "#data_dir = '/home/ubuntu/data/dogscats/sample'\n",
    "data_dir = '/home/ubuntu/data/dogscats'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```datasets``` is a class of the ```torchvision``` package (see [torchvision.datasets](http://pytorch.org/docs/master/torchvision/datasets.html)) and deals with data loading. It integrates a multi-threaded loader that fetches images from the disk, groups them in mini-batches and serves them continously to the GPU right after each _forward_/_backward_ pass through the network. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "dsets = {x: datasets.ImageFolder(os.path.join(data_dir, x), prep1)\n",
    "         for x in ['train', 'valid']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/ubuntu/data/dogscats/train'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.path.join(data_dir,'train')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "?datasets.ImageFolder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['cats', 'dogs']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dsets['train'].classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'cats': 0, 'dogs': 1}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dsets['train'].class_to_idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('/home/ubuntu/data/dogscats/train/cats/cat.0.jpg', 0),\n",
       " ('/home/ubuntu/data/dogscats/train/cats/cat.1.jpg', 0),\n",
       " ('/home/ubuntu/data/dogscats/train/cats/cat.10.jpg', 0),\n",
       " ('/home/ubuntu/data/dogscats/train/cats/cat.100.jpg', 0),\n",
       " ('/home/ubuntu/data/dogscats/train/cats/cat.1000.jpg', 0)]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dsets['train'].imgs[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'train': 23000, 'valid': 2000}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dset_sizes = {x: len(dsets[x]) for x in ['train', 'valid']}\n",
    "dset_sizes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "dset_classes = dsets['train'].classes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ```torchvision``` packages allows complex pre-processing/transforms of the input data (_e.g._ normalization, cropping, flipping, jittering). A sequence of transforms can be grouped in a pipeline with the help of the ```torchvision.transforms.Compose``` function, see [torchvision.transforms](http://pytorch.org/docs/master/torchvision/transforms.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "?prep1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "dset_loaders = {x: torch.utils.data.DataLoader(dsets[x], batch_size=64,\n",
    "                                               shuffle=shuffle_valtrain(x), num_workers=6)\n",
    "                for x in ['train', 'valid']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "?torch.utils.data.DataLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_valid = torch.utils.data.DataLoader(dsets['valid'], batch_size=5, shuffle=True, num_workers=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,"
     ]
    }
   ],
   "source": [
    "count = 1\n",
    "for data in dataset_valid:\n",
    "    print(count, end=\",\")\n",
    "    if count == 1:\n",
    "        inputs_try,labels_try = data\n",
    "    count +=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0, 1, 0, 1, 0])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels_try"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([5, 3, 224, 224])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputs_try.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make a grid from batch\n",
    "out = torchvision.utils.make_grid(inputs_try)\n",
    "\n",
    "imshow(out, title=[dset_classes[x] for x in labels_try])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get a batch of training data\n",
    "inputs, classes = next(iter(dset_loaders['train']))\n",
    "\n",
    "n_images = 8\n",
    "\n",
    "# Make a grid from batch\n",
    "out = torchvision.utils.make_grid(inputs[0:n_images])\n",
    "\n",
    "imshow(out, title=[dset_classes[x] for x in classes[0:n_images]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get a batch of validation data\n",
    "inputs, classes = next(iter(dset_loaders['valid']))\n",
    "\n",
    "n_images = 8\n",
    "\n",
    "# Make a grid from batch\n",
    "out = torchvision.utils.make_grid(inputs[0:n_images])\n",
    "\n",
    "imshow(out, title=[dset_classes[x] for x in classes[0:n_images]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating VGG Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The torchvision module comes with a zoo of popular CNN architectures which are already trained on [ImageNet](http://www.image-net.org/) (1.2M training images). When called the first time, if ```pretrained=True``` the model is fetched over the internet and downloaded to ```~/.torch/models```.\n",
    "For next calls, the model will be directly read from there."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_vgg = models.vgg16(pretrained=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will first use VGG Model without any modification. In order to interpret the results, we need to import the 1000 ImageNet categories, available at: [https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json](https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2018-09-11 11:37:28--  https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json\n",
      "Resolving s3.amazonaws.com (s3.amazonaws.com)... 52.216.65.187\n",
      "Connecting to s3.amazonaws.com (s3.amazonaws.com)|52.216.65.187|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 35363 (35K) [application/octet-stream]\n",
      "Saving to: ‘imagenet_class_index.json.1’\n",
      "\n",
      "100%[======================================>] 35,363       195KB/s   in 0.2s   \n",
      "\n",
      "2018-09-11 11:37:29 (195 KB/s) - ‘imagenet_class_index.json.1’ saved [35363/35363]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#to do once:\n",
    "#%cd /home/ubuntu/data/\n",
    "#!wget https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "fpath = '/home/ubuntu/data/imagenet_class_index.json'\n",
    "\n",
    "with open(fpath) as f:\n",
    "    class_dict = json.load(f)\n",
    "dic_imagenet = [class_dict[str(i)][1] for i in range(len(class_dict))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['tench', 'goldfish', 'great_white_shark', 'tiger_shark']"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dic_imagenet[:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs_try , labels_try = var_cgpu(inputs_try,use_gpu),var_cgpu(labels_try,use_gpu)\n",
    "\n",
    "if use_gpu:\n",
    "    model_vgg = model_vgg.cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "outputs_try = model_vgg(inputs_try)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[-1.8098, -0.7815, -1.8770,  ..., -4.1540,  1.8534,  5.7892],\n",
       "        [-1.8494, -2.4570, -1.2012,  ..., -0.7277,  0.9933,  1.0415],\n",
       "        [ 0.5598, -0.4154,  2.0784,  ...,  0.3922,  0.5171,  2.7785],\n",
       "        [-2.4766, -1.8761, -0.5674,  ..., -1.1757, -0.4506,  5.1015],\n",
       "        [-1.1245,  2.3316, -0.8116,  ..., -3.5351,  3.0400,  6.7602]],\n",
       "       device='cuda:0', grad_fn=<ThAddmmBackward>)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outputs_try"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "m_softm = nn.Softmax(dim=1)\n",
    "vals_try,preds_try = torch.max(m_softm(outputs_try.data),1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0.7623, 0.5654, 0.2085, 0.1119, 0.5020], device='cuda:0')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vals_try"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['tabby', 'chow', 'Angora', 'face_powder', 'tabby']\n"
     ]
    }
   ],
   "source": [
    "print([dic_imagenet[i] for i in preds_try.data])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "out = torchvision.utils.make_grid(inputs_try.data.cpu())\n",
    "\n",
    "imshow(out, title=[dset_classes[x] for x in labels_try.data.cpu()])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modifying the last layer and setting the gradient false to all layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VGG(\n",
      "  (features): Sequential(\n",
      "    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (1): ReLU(inplace)\n",
      "    (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (3): ReLU(inplace)\n",
      "    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (6): ReLU(inplace)\n",
      "    (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (8): ReLU(inplace)\n",
      "    (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (11): ReLU(inplace)\n",
      "    (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (13): ReLU(inplace)\n",
      "    (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (15): ReLU(inplace)\n",
      "    (16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (18): ReLU(inplace)\n",
      "    (19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (20): ReLU(inplace)\n",
      "    (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (22): ReLU(inplace)\n",
      "    (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (25): ReLU(inplace)\n",
      "    (26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (27): ReLU(inplace)\n",
      "    (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (29): ReLU(inplace)\n",
      "    (30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  )\n",
      "  (classifier): Sequential(\n",
      "    (0): Linear(in_features=25088, out_features=4096, bias=True)\n",
      "    (1): ReLU(inplace)\n",
      "    (2): Dropout(p=0.5)\n",
      "    (3): Linear(in_features=4096, out_features=4096, bias=True)\n",
      "    (4): ReLU(inplace)\n",
      "    (5): Dropout(p=0.5)\n",
      "    (6): Linear(in_features=4096, out_features=1000, bias=True)\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "print(model_vgg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll learn about what these different blocks do later in the course. For now, it's enough to know that:\n",
    "\n",
    "- Convolution layers are for finding small to medium size patterns in images -- analyzing the images locally\n",
    "- Dense (fully connected) layers are for combining patterns across an image -- analyzing the images globally\n",
    "- Pooling layers downsample -- in order to reduce image size and to improve invariance of learned features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An illustration taken from [Deep Learning with PaddlePaddle](http://book.paddlepaddle.org/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src='vgg16.png'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this practical example, our goal is to use the already trained model and just change the number of output classes. To this end we replace the last ```nn.Linear``` layer trained for 1000 classes to ones with 2 classes. In order to freeze the weights of the other layers during training, we set the field ```required_grad=False```. In this manner no gradient will be computed for them during backprop and hence no update in the weights. Only the weights for the 2 class layer will be updated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "for param in model_vgg.parameters():\n",
    "    param.requires_grad = False\n",
    "model_vgg.classifier._modules['6'] = nn.Linear(4096, 2)\n",
    "#model_vgg.classifier[6].out_features = 2\n",
    "#for param in model_vgg.classifier[6].parameters():\n",
    "#    param.requires_grad = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sequential(\n",
      "  (0): Linear(in_features=25088, out_features=4096, bias=True)\n",
      "  (1): ReLU(inplace)\n",
      "  (2): Dropout(p=0.5)\n",
      "  (3): Linear(in_features=4096, out_features=4096, bias=True)\n",
      "  (4): ReLU(inplace)\n",
      "  (5): Dropout(p=0.5)\n",
      "  (6): Linear(in_features=4096, out_features=2, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "print(model_vgg.classifier)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "if use_gpu:\n",
    "    model_vgg = model_vgg.cuda()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculating preconvoluted features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preconvfeat(dataset):\n",
    "    conv_features = []\n",
    "    labels_list = []\n",
    "    for data in dataset:\n",
    "        inputs,labels = data\n",
    "        if use_gpu:\n",
    "            inputs , labels = Variable(inputs.cuda()),Variable(labels.cuda())\n",
    "        else:\n",
    "            inputs , labels = Variable(inputs),Variable(labels)\n",
    "        \n",
    "        x = model_vgg.features(inputs)\n",
    "        conv_features.extend(x.data.cpu().numpy())\n",
    "        labels_list.extend(labels.data.cpu().numpy())\n",
    "    conv_features = np.concatenate([[feat] for feat in conv_features])\n",
    "    return (conv_features,labels_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_try = model_vgg.features(inputs_try)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "?x_try"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([5, 512, 7, 7])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_try.data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25088"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "7*7*512"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0, 1, 0, 1, 0], device='cuda:0')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels_try"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0, 1, 0, 1, 0], device='cuda:0')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels_try.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3min 8s, sys: 22.2 s, total: 3min 30s\n",
      "Wall time: 3min 31s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "conv_feat_train,labels_train = preconvfeat(dset_loaders['train'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 16.4 s, sys: 2.16 s, total: 18.6 s\n",
      "Wall time: 19.5 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "conv_feat_val,labels_val = preconvfeat(dset_loaders['valid'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Warning to be modified!\n",
    "#%mkdir /home/ubuntu/data/dogscats/vgg16\n",
    "#%mkdir "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_array(data_dir+'/vgg16/conv_feat_train.bc',conv_feat_train)\n",
    "save_array(data_dir+'/vgg16/labels_train.bc',labels_train)\n",
    "save_array(data_dir+'/vgg16/conv_feat_val.bc',conv_feat_val)\n",
    "save_array(data_dir+'/vgg16/labels_val.bc',labels_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading Preconvoluted features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "conv_feat_train = load_array(data_dir+'/vgg16/conv_feat_train.bc')\n",
    "labels_train = load_array(data_dir+'/vgg16/labels_train.bc')\n",
    "conv_feat_val = load_array(data_dir+'/vgg16/conv_feat_val.bc')\n",
    "labels_val = load_array(data_dir+'/vgg16/labels_val.bc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(23000, 512, 7, 7)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conv_feat_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training fully connected module"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating loss function and optimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "criterion = nn.CrossEntropyLoss()\n",
    "lr = 0.01\n",
    "optimizer_vgg = torch.optim.SGD(model_vgg.classifier[6].parameters(),lr = lr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating Data generator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "def data_gen(conv_feat,labels,batch_size=64,shuffle=True):\n",
    "    labels = np.array(labels)\n",
    "    if shuffle:\n",
    "        index = np.random.permutation(len(conv_feat))\n",
    "        conv_feat = conv_feat[index]\n",
    "        labels = labels[index]\n",
    "    for idx in range(0,len(conv_feat),batch_size):\n",
    "        yield(conv_feat[idx:idx+batch_size],labels[idx:idx+batch_size])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_model(model,size,conv_feat=None,labels=None,epochs=1,optimizer=None,train=True,shuffle=True):\n",
    "    if train:\n",
    "        model.train()\n",
    "    else:\n",
    "        model.eval()\n",
    "        \n",
    "    for epoch in range(epochs):\n",
    "        batches = data_gen(conv_feat=conv_feat,labels=labels,shuffle=shuffle)\n",
    "        total = 0\n",
    "        running_loss = 0.0\n",
    "        running_corrects = 0\n",
    "        for inputs,classes in batches:\n",
    "            if use_gpu:\n",
    "                inputs , classes = torch.from_numpy(inputs).cuda(), torch.from_numpy(classes).cuda()\n",
    "            else:\n",
    "                inputs , classes = torch.from_numpy(inputs), torch.from_numpy(classes)\n",
    "                \n",
    "            inputs = inputs.view(inputs.size(0), -1)\n",
    "            outputs = model(inputs)\n",
    "            loss = criterion(outputs,classes)           \n",
    "            if train:\n",
    "                if optimizer is None:\n",
    "                    raise ValueError('Pass optimizer for train mode')\n",
    "                optimizer = optimizer\n",
    "                optimizer.zero_grad()\n",
    "                loss.backward()\n",
    "                optimizer.step()\n",
    "            _,preds = torch.max(outputs.data,1)\n",
    "            # statistics\n",
    "            running_loss += loss.data.item()\n",
    "            running_corrects += torch.sum(preds == classes.data)\n",
    "        epoch_loss = running_loss / size\n",
    "        epoch_acc = running_corrects.data.item() / size\n",
    "        print('Loss: {:.4f} Acc: {:.4f}'.format(\n",
    "                     epoch_loss, epoch_acc))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss: 0.0012 Acc: 0.9711\n",
      "Loss: 0.0009 Acc: 0.9781\n",
      "Loss: 0.0009 Acc: 0.9768\n",
      "Loss: 0.0008 Acc: 0.9796\n",
      "Loss: 0.0008 Acc: 0.9780\n",
      "Loss: 0.0008 Acc: 0.9787\n",
      "Loss: 0.0008 Acc: 0.9797\n",
      "Loss: 0.0008 Acc: 0.9798\n",
      "Loss: 0.0008 Acc: 0.9799\n",
      "Loss: 0.0008 Acc: 0.9809\n",
      "CPU times: user 36.2 s, sys: 14.5 s, total: 50.8 s\n",
      "Wall time: 50.7 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "(train_model(model=model_vgg.classifier,size=dset_sizes['train'],conv_feat=conv_feat_train,labels=labels_train,\n",
    "            epochs=10,optimizer=optimizer_vgg,train=True,shuffle=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss: 0.0005 Acc: 0.9845\n"
     ]
    }
   ],
   "source": [
    "train_model(conv_feat=conv_feat_val,labels=labels_val,model=model_vgg.classifier\n",
    "            ,size=dset_sizes['valid'],train=False,shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get a batch of training data\n",
    "inputs, classes = next(iter(dset_loaders['valid']))\n",
    "\n",
    "out = torchvision.utils.make_grid(inputs[0:n_images])\n",
    "\n",
    "imshow(out, title=[dset_classes[x] for x in classes[0:n_images]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[9.6986e-01, 3.0139e-02],\n",
      "        [1.0000e+00, 6.2044e-09],\n",
      "        [9.9960e-01, 4.0487e-04],\n",
      "        [1.0000e+00, 3.9877e-06],\n",
      "        [9.9957e-01, 4.2587e-04],\n",
      "        [1.0000e+00, 3.0642e-06],\n",
      "        [9.9977e-01, 2.2547e-04],\n",
      "        [1.0000e+00, 7.9461e-07]], device='cuda:0', grad_fn=<SoftmaxBackward>)\n"
     ]
    }
   ],
   "source": [
    "inputs = Variable(torch.from_numpy(conv_feat_val[:n_images]))\n",
    "inputs = inputs.view(inputs.size(0), -1)\n",
    "if use_gpu:\n",
    "    outputs = model_vgg.classifier(inputs.cuda())\n",
    "else:\n",
    "    outputs = model_vgg.classifier(inputs)\n",
    "print(m_softm(outputs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 0, 0, 0, 0, 0, 0, 0]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels_val[:n_images]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating VGG model by hand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VGG_16(\n",
      "  (convblock1): Sequential(\n",
      "    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (1): ReLU(inplace)\n",
      "    (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (3): ReLU(inplace)\n",
      "    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  )\n",
      "  (convblock2): Sequential(\n",
      "    (0): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (1): ReLU(inplace)\n",
      "    (2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (3): ReLU(inplace)\n",
      "    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  )\n",
      "  (convblock3): Sequential(\n",
      "    (0): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (1): ReLU(inplace)\n",
      "    (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (3): ReLU(inplace)\n",
      "    (4): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (5): ReLU(inplace)\n",
      "    (6): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  )\n",
      "  (convblock4): Sequential(\n",
      "    (0): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (1): ReLU(inplace)\n",
      "    (2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (3): ReLU(inplace)\n",
      "    (4): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (5): ReLU(inplace)\n",
      "    (6): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  )\n",
      "  (convblock5): Sequential(\n",
      "    (0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (1): ReLU(inplace)\n",
      "    (2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (3): ReLU(inplace)\n",
      "    (4): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (5): ReLU(inplace)\n",
      "    (6): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  )\n",
      "  (classifier): Sequential(\n",
      "    (0): Linear(in_features=25088, out_features=4096, bias=True)\n",
      "    (1): ReLU(inplace)\n",
      "    (2): Dropout(p=0.5)\n",
      "    (3): Linear(in_features=4096, out_features=4096, bias=True)\n",
      "    (4): ReLU(inplace)\n",
      "    (5): Dropout(p=0.5)\n",
      "    (6): Linear(in_features=4096, out_features=1000, bias=True)\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "\n",
    "class VGG_16(nn.Module):\n",
    "\n",
    "    def __init__(self,num_classes=1000):\n",
    "        super(VGG_16, self).__init__()\n",
    "        self.convblock1 = nn.Sequential(\n",
    "            nn.Conv2d(3, 64, kernel_size=3, padding=1),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Conv2d(64, 64, kernel_size=3, padding=1),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.MaxPool2d(kernel_size=2, stride=2),\n",
    "        )\n",
    "        self.convblock2 = nn.Sequential(\n",
    "            nn.Conv2d(64, 128, kernel_size=3, padding=1),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Conv2d(128, 128, kernel_size=3, padding=1),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.MaxPool2d(kernel_size=2, stride=2),\n",
    "        )\n",
    "        self.convblock3 = nn.Sequential(\n",
    "            nn.Conv2d(128, 256, kernel_size=3, padding=1),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Conv2d(256, 256, kernel_size=3, padding=1),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Conv2d(256, 256, kernel_size=3, padding=1),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.MaxPool2d(kernel_size=2, stride=2),\n",
    "        )\n",
    "        self.convblock4 = nn.Sequential(\n",
    "            nn.Conv2d(256, 512, kernel_size=3, padding=1),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Conv2d(512, 512, kernel_size=3, padding=1),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Conv2d(512, 512, kernel_size=3, padding=1),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.MaxPool2d(kernel_size=2, stride=2),\n",
    "        )\n",
    "        self.convblock5 = nn.Sequential(\n",
    "            nn.Conv2d(512, 512, kernel_size=3, padding=1),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Conv2d(512, 512, kernel_size=3, padding=1),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Conv2d(512, 512, kernel_size=3, padding=1),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.MaxPool2d(kernel_size=2, stride=2),\n",
    "        )\n",
    "        self.classifier = nn.Sequential(\n",
    "            nn.Linear(512 * 7 * 7, 4096),\n",
    "            nn.ReLU(True),\n",
    "            nn.Dropout(),\n",
    "            nn.Linear(4096, 4096),\n",
    "            nn.ReLU(True),\n",
    "            nn.Dropout(),\n",
    "            nn.Linear(4096, num_classes),\n",
    "        )       \n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.convblock1(x)\n",
    "        x = self.convblock2(x)\n",
    "        x = self.convblock3(x)\n",
    "        x = self.convblock4(x)\n",
    "        x = self.convblock5(x)\n",
    "        x = x.view(x.size(0), -1)\n",
    "        x = self.classifier(x)\n",
    "        return x\n",
    "    \n",
    "    def init_weight(self,w):\n",
    "        i=0\n",
    "        for idx, m in enumerate(self.children()):\n",
    "            for idy, msub in enumerate(m.children()):\n",
    "                classname = msub.__class__.__name__\n",
    "                if classname.find('Conv') != -1:\n",
    "                    msub.weight.data = w['features.'+str(i)+'.weight']#.clone()\n",
    "                    msub.bias.data = w['features.'+str(i)+'.bias']#.clone()\n",
    "                    print(msub,'features.'+str(i))\n",
    "                if classname.find('Linear') != -1:\n",
    "                    msub.weight.data = w['classifier.'+str(i-31)+'.weight']#.clone()\n",
    "                    msub.bias.data = w['classifier.'+str(i-31)+'.bias']#.clone()\n",
    "                    print(msub,'classifier.'+str(i-31))\n",
    "                i +=1\n",
    "        \n",
    "\n",
    "net = VGG_16()\n",
    "print(net)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filling our model with learned weights\n",
    "\n",
    "Now that we created VGG model, we do not want to train it from scratch. Fortunately, this has been done for us and we only need to get the weights and fill them properly which is the purpose of the init_weight method defined above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.utils.model_zoo as model_zoo\n",
    "weights = model_zoo.load_url('https://download.pytorch.org/models/vgg16-397923af.pth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "collections.OrderedDict"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) features.0\n",
      "Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) features.2\n",
      "Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) features.5\n",
      "Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) features.7\n",
      "Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) features.10\n",
      "Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) features.12\n",
      "Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) features.14\n",
      "Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) features.17\n",
      "Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) features.19\n",
      "Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) features.21\n",
      "Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) features.24\n",
      "Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) features.26\n",
      "Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) features.28\n",
      "Linear(in_features=25088, out_features=4096, bias=True) classifier.0\n",
      "Linear(in_features=4096, out_features=4096, bias=True) classifier.3\n",
      "Linear(in_features=4096, out_features=1000, bias=True) classifier.6\n"
     ]
    }
   ],
   "source": [
    "net.init_weight(weights)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To understand what the init_weight method is doing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "odict_keys(['features.0.weight', 'features.0.bias', 'features.2.weight', 'features.2.bias', 'features.5.weight', 'features.5.bias', 'features.7.weight', 'features.7.bias', 'features.10.weight', 'features.10.bias', 'features.12.weight', 'features.12.bias', 'features.14.weight', 'features.14.bias', 'features.17.weight', 'features.17.bias', 'features.19.weight', 'features.19.bias', 'features.21.weight', 'features.21.bias', 'features.24.weight', 'features.24.bias', 'features.26.weight', 'features.26.bias', 'features.28.weight', 'features.28.bias', 'classifier.0.weight', 'classifier.0.bias', 'classifier.3.weight', 'classifier.3.bias', 'classifier.6.weight', 'classifier.6.bias'])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weights.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 -> Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "1 -> ReLU (inplace)\n",
      "2 -> Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "3 -> ReLU (inplace)\n",
      "4 -> MaxPool2d (size=(2, 2), stride=(2, 2), dilation=(1, 1))\n",
      "5 -> Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "6 -> ReLU (inplace)\n",
      "7 -> Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "8 -> ReLU (inplace)\n",
      "9 -> MaxPool2d (size=(2, 2), stride=(2, 2), dilation=(1, 1))\n",
      "10 -> Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "11 -> ReLU (inplace)\n",
      "12 -> Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "13 -> ReLU (inplace)\n",
      "14 -> Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "15 -> ReLU (inplace)\n",
      "16 -> MaxPool2d (size=(2, 2), stride=(2, 2), dilation=(1, 1))\n",
      "17 -> Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "18 -> ReLU (inplace)\n",
      "19 -> Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "20 -> ReLU (inplace)\n",
      "21 -> Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "22 -> ReLU (inplace)\n",
      "23 -> MaxPool2d (size=(2, 2), stride=(2, 2), dilation=(1, 1))\n",
      "24 -> Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "25 -> ReLU (inplace)\n",
      "26 -> Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "27 -> ReLU (inplace)\n",
      "28 -> Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "29 -> ReLU (inplace)\n",
      "30 -> MaxPool2d (size=(2, 2), stride=(2, 2), dilation=(1, 1))\n",
      "31 -> Linear (25088 -> 4096)\n",
      "32 -> ReLU (inplace)\n",
      "33 -> Dropout (p = 0.5)\n",
      "34 -> Linear (4096 -> 4096)\n",
      "35 -> ReLU (inplace)\n",
      "36 -> Dropout (p = 0.5)\n",
      "37 -> Linear (4096 -> 1000)\n"
     ]
    }
   ],
   "source": [
    "inc=0\n",
    "for idx, m in enumerate(net.children()):\n",
    "    for idy, msub in enumerate(m.children()):\n",
    "        print(inc, '->', msub)\n",
    "        inc +=1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now our model is ready for use, let's try it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "for param in net.parameters():\n",
    "    param.requires_grad = False\n",
    "net.classifier._modules['6'] = nn.Linear(4096, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VGG_16(\n",
       "  (convblock1): Sequential(\n",
       "    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (1): ReLU(inplace)\n",
       "    (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (3): ReLU(inplace)\n",
       "    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "  )\n",
       "  (convblock2): Sequential(\n",
       "    (0): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (1): ReLU(inplace)\n",
       "    (2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (3): ReLU(inplace)\n",
       "    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "  )\n",
       "  (convblock3): Sequential(\n",
       "    (0): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (1): ReLU(inplace)\n",
       "    (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (3): ReLU(inplace)\n",
       "    (4): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (5): ReLU(inplace)\n",
       "    (6): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "  )\n",
       "  (convblock4): Sequential(\n",
       "    (0): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (1): ReLU(inplace)\n",
       "    (2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (3): ReLU(inplace)\n",
       "    (4): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (5): ReLU(inplace)\n",
       "    (6): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "  )\n",
       "  (convblock5): Sequential(\n",
       "    (0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (1): ReLU(inplace)\n",
       "    (2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (3): ReLU(inplace)\n",
       "    (4): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (5): ReLU(inplace)\n",
       "    (6): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "  )\n",
       "  (classifier): Sequential(\n",
       "    (0): Linear(in_features=25088, out_features=4096, bias=True)\n",
       "    (1): ReLU(inplace)\n",
       "    (2): Dropout(p=0.5)\n",
       "    (3): Linear(in_features=4096, out_features=4096, bias=True)\n",
       "    (4): ReLU(inplace)\n",
       "    (5): Dropout(p=0.5)\n",
       "    (6): Linear(in_features=4096, out_features=2, bias=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "if use_gpu:\n",
    "    net = net.cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer_net = torch.optim.SGD(net.classifier[6].parameters(),lr = lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss: 0.0012 Acc: 0.9708\n",
      "Loss: 0.0009 Acc: 0.9775\n",
      "Loss: 0.0009 Acc: 0.9785\n",
      "Loss: 0.0008 Acc: 0.9801\n",
      "Loss: 0.0008 Acc: 0.9776\n",
      "Loss: 0.0008 Acc: 0.9807\n",
      "Loss: 0.0008 Acc: 0.9807\n",
      "Loss: 0.0008 Acc: 0.9790\n",
      "Loss: 0.0008 Acc: 0.9823\n",
      "Loss: 0.0008 Acc: 0.9810\n",
      "CPU times: user 35.9 s, sys: 14.6 s, total: 50.5 s\n",
      "Wall time: 50.4 s\n"
     ]
    }
   ],
   "source": [
    "# I am cheating a bit here as you should redo the computation of the features \n",
    "#if you want to check that you built the right NN. Homework ;-)\n",
    "%%time\n",
    "(train_model(model=net.classifier,size=dset_sizes['train'],conv_feat=conv_feat_train,labels=labels_train,\n",
    "            epochs=10,optimizer=optimizer_net,train=True,shuffle=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see what it gives on our 'try' sample (5 random images from the validation set)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "#to be modified if on CPU! remove .cuda()\n",
    "new_outputs_try = net(inputs_try.cuda())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 5.2026, -6.0008],\n",
       "        [-2.2698,  2.0120],\n",
       "        [ 0.6424,  0.4140],\n",
       "        [-0.0173,  0.0924],\n",
       "        [ 5.8523, -5.8899]], device='cuda:0')"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_outputs_try.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_vals_try,new_preds_try = torch.max(m_softm(new_outputs_try.data),1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0, 1, 0, 1, 0], device='cuda:0')"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_preds_try"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#corresponding images\n",
    "out = torchvision.utils.make_grid(inputs_try.data.cpu())\n",
    "\n",
    "imshow(out, title=[dset_classes[x] for x in labels_try.data.cpu()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([1.0000, 0.9864, 0.5569, 0.5274, 1.0000], device='cuda:0')"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_vals_try"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Environment (conda_pytorch_p36)",
   "language": "python",
   "name": "conda_pytorch_p36"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
